17 research outputs found
Effect of Fiber Orientation on Tribological Performance of Abaca Fiber Reinforced Epoxy Composite Under Dry Contact Conditions
No full textAbstract: Today, natural fibers and their composites are increasingly present in the industrial production of various materials, ranging from the textile industry, through construction, all the way to the automotive and aerospace industry. Their wide application is justified by the replacement capabilities of composite materials reinforced with synthetic fibers. This paper presents tribological research of abaca fiber reinforced epoxy composite material. Fiber orientation and its effect on the tribological performances of the composite was analyzed. The extremely low viscosity epoxy resin reinforced with NaOH treated long abaca fibers is investigated under the different operating conditions. The unidirectional abaca fibers reinforced the epoxy resin and formed composite specimens with fibers in three directions, Parallel (P-O), Anti-Parallel (AP-O), and Normal (N-O), respecting the sliding direction. The specimens were fabricated by fiber volume fraction of 30 wt% using the vacuum infusion technique. The block- on-disc (BOD) apparatus was employed to exhibit the tribological tests. To conduct the test, a normal load of 35N and 45N was applied. The experimental results showed that the presence of abaca fiber significantly improved the wear characteristics of the matrix than the neat epoxy. An increased coefficient of friction was observed in samples with anti-parallel oriented fibers at an
applied load of 35N. The conducted research showed that the use of abaca fibers as fillers could improve the tribological characteristics of the epoxy resin-based composite material
A Bayesian latent time-series model for switching temporal interaction analysis
No full textThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 153-157).We introduce a Bayesian discrete-time framework for switching-interaction analysis under uncertainty, in which latent interactions, switching pattern and signal states and dynamics are inferred from noisy and possibly missing observations of these signals. We propose reasoning over posterior distribution of these latent variables as a means of combating and characterizing uncertainty. This approach also allows for answering a variety of questions probabilistically, which is suitable for exploratory pattern discovery and post-analysis by human experts. This framework is based on a Bayesian learning of the structure of a switching dynamic Bayesian network (DBN) and utilizes a states-pace approach to allow for noisy observations and missing data. It generalizes the autoregressive switching interaction model of Siracusa et al. [50], which does not allow observation noise, and the switching linear dynamic system model of Fox et al. [16], which does not infer interactions among signals. We develop a Gibbs sampling inference procedure, which is particularly efficient in the case of linear Gaussian dynamics and observation models. We use a modular prior over structures and a bound on the number of parent sets per signal to reduce the number of structures to consider from super-exponential to polynomial. We provide a procedure for setting the parameters of the prior and initializing latent variables that leads to a successful application of the inference algorithm in practice, and leaves only few general parameters to be set by the user. A detailed analysis of the computational and memory complexity of each step of the algorithm is also provided. We demonstrate the utility of our framework on different types of data. Different benefits of the proposed approach are illustrated using synthetic data. Most real data do not contain annotation of interactions. To demonstrate the ability of the algorithm to infer interactions and the switching pattern from time-series data in a realistic setting, joystick data is created, which is a controlled, human-generated data that implies ground truth annotations by design. Climate data is a real data used to illustrate the variety of applications and types of analyses enabled by the developed methodology. Finally, we apply the developed model to the problem of structural health monitoring in civil engineering. Time-series data from accelerometers located at multiple positions on a building are obtained for two laboratory model structures and a real building. We analyze the results of interaction analysis and how the inferred dependencies among sensor signals relate to the physical structure and properties of the building, as well as the environment and excitation conditions. We develop time-series classification and single-class classification extensions of the model and apply them to the problem of damage detection. We show that the method distinguishes time-series obtained under different conditions with high accuracy, in both supervised and single-class classification setups.by Zoran Dzunic.Ph. D
Text structure-aware classification
No full textThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 73-76).Bag-of-words representations are used in many NLP applications, such as text classification and sentiment analysis. These representations ignore relations across different sentences in a text and disregard the underlying structure of documents. In this work, we present a method for text classification that takes into account document structure and only considers segments that contain information relevant for a classification task. In contrast to the previous work, which assumes that relevance annotation is given, we perform the relevance prediction in an unsupervised fashion. We develop a Conditional Bayesian Network model that incorporates relevance as a hidden variable of a target classifier. Relevance and label predictions are performed jointly, optimizing the relevance component for the best result of the target classifier. Our work demonstrates that incorporating structural information in document analysis yields significant performance gains over bag-of-words approaches on some NLP tasks.by Zoran Dzunic.S.M
Multipoint methods for solving nonlinear equations: a survey
No full textApplied Mathematics and Computation, 226, (2014), 635–640.The article of record as published may be located at http://dx.doi.org/10.1016/j.amc.2013.10.072Multipoint iterative methods belong to the class of the most efficient methods for solving nonlinear equations. Recent interest in the research and development of this type of meth- ods has arisen from their capability to overcome theoretical limits of one-point methods concerning the convergence order and computational efficiency. This survey paper is a mixture of theoretical results and algorithmic aspects and it is intended as a review of the most efficient root-finding algorithms and developing techniques in a general sense. Many existing methods of great efficiency appear as special cases of presented general iter- ative schemes. Special attention is devoted to multipoint methods with memory that use already computed information to considerably increase convergence rate without addi- tional computational costs. Some classical results of the 1970s which have had a great influence to the topic, often neglected or unknown to many readers, are also included not only as historical notes but also as genuine sources of many recent ideas. To a certain degree, the presented study follows in parallel main themes shown in the recently pub- lished book (Petkovic ́ et al., 2013) [53], written by the authors of this paper
Micro/nanoscale structural, mechanical and tribological characterization of ZA-27/SiC nanocomposites
No full text© The Author(s) 2019. The structural, mechanical and tribological properties of ZA-27/SiC nanocomposites were investigated at micro/nanoscale. The nanocomposites with different volume fractions of nano-sized SiC particles were produced using the compocasting technique. The microstructure of nanocomposites was characterized with formation of SiC nano agglomerates, which were relatively uniformly distributed. The increase in SiC content contributed to the uniformity of their distribution. Also, the phenomenon of particle segregation in the form of particle-rich clusters, as well as particle-porosity clusters, was identified. The density level of composites decreased with the increase of the SiC content. The porosity followed a reverse trend. The tendency for formation of local particle-porosity clusters was the highest in ZA-27/1% SiC nanocomposite, causing the highest level of porosity. Increasing percentage of SiC content was followed by the increase in micro/nanohardness of the composites. The results of micro/nanoscale tribotests revealed that the reinforcing with SiC nanoparticles significantly improved wear and friction behavior of ZA-27 matrix alloy. The rate of improvement increased with the increase of SiC nanoparticle content, load, and sliding speed. The highest degree of changes corresponded to the change of the SiC nanoparticle content from 0 to 1 wt%. The further decrease of wear with SiC content (from 1 to 5 wt%) was almost linear. The different tribological behavior of tested ZA-27 matrix and ZA-27/SiC nanocomposites was influenced by differences of intensity of adhesion resulted in transferred layers of matrix material onto worn surfaces of Al2O3 ball counterpart. The intensity of adhesion significantly decreased with the increase of SiC nanoparticle content
Educational attainment as a predictor of poverty and social exclusion: Empirical analysis of Serbian case
No full textThis study examines the impact of education on the risk of poverty and social exclusion in a single-country framework. Relying on household and individual level data from the annual EU-SILC survey obtained in Serbia in 2020, we estimate the market and non-market benefits of education in the context of combating poverty and social exclusion in developing countries. Based on a representative sample of the adult population in Serbia, we explore to what extent the risk of poverty and social exclusion can be predicted by the levels of educational attainment. Econometric estimations indicate that educational underachievement acts as a significant driver of poverty and social exclusion. Probit regression analysis indicates that the risk of experiencing poverty and social exclusion decreases substantially with higher education levels. We include three model specifications that calculate the predicted probability of being at risk of poverty, severely materially deprived and exposed to combined risks. Holding other predictors constant, the decrease in poverty and social exclusion probability attributed to a one level increase in educational attainment amounts up to 7.96% (for unemployed women with only primary education). The analysis confirms that the highest gains from schooling are materialized for the categories of respondents who are not active in the labor market and those with the lowest levels of educational attainment. Besides this, self-perceived health and labor market activity significantly affect the risk of poverty, material deprivation and social exclusion. The impact of age differs across our model specifications, indicating that age increases the probability of severe material deprivation and the combined risk of poverty and deprivation, while older age appears to go in hand with a lower risk of poverty itself. These results offer relevant information that should be considered when determining the optimal level of social investment in education
Influence of Al2o3 Particle Content on the Sliding Wear Behaviour of Za-27 Alloy Composites
No full textThe lubricated and dry sliding wear behaviour of ZA-27 alloy composites reinforced with Al2O3 particles of size 250 mu m was evaluated. The content of Al2O3 particles in the alloy was 3, 5 and 10 wt.%. Composites were produced by the compocasting process using mechanical mixing of the matrix, i.e. Al2O3 particles as reinforcement were added into the semi-solid ZA-27 alloy by infiltration and admixing. A block-on-disc wear test device was used to evaluate the wear rate, whereat 30CrNiMo8 steel disc was used as the counterface, under dry and lubricated sliding conditions at different specific loads and sliding speeds. Results indicated that the wear rates of the composites were lower than those of the matrix alloy and further decreased with the increase in Al2O3 particles content in all combinations of applied loads and sliding speeds both in dry and lubricated tests
Modeling and Prediction of Surface Roughness in the End Milling Process using Multiple Regression Analysis and Artificial Neural Network
No full textIn recent years, trends have been towards modeling machine processing using artificial intelligence. Artificial neural network (ANN) and multiple regression analysis are methods used to model and optimize the performance of manufacturing technologies. ANN and multiple regression analysis show high reliability in the prediction and optimization of machining processes. In this paper, machining parameters such as spindle speed, feed rate and depth of cut were used in end milling process to minimize surface roughness. The influence of the parameters on the surface roughness was investigated using an artificial neural network and multiple regression analysis, and results are compared with the measured results
IN VIVO STUDY OF THE NANOMECHANICAL PROPERTIES OF LEUCITE GLASS CERAMIC PREPARED WITH DIFFERENT SURFACE FINISHING PROCEDURES
No full textThis paper reports the nanomechanical properties of Leucite glass ceramic prepared with three different surface finishing procedures: polishing, glazing and grinding, using the Nanoindenter. Also, AFM analysis was done in order to determine the roughness parameter Ra. The nanoindentation were performed in order to define the hardness (HV) and Young's modulus (E) of the surface structure using Berkovich diamond pyramid and the experiment was organized in a 3x4 array. Indentation imprints were investigated using the optical and Atomic Force Microscopy. The obtained results of nanomechanical properties mostly depend of applied surface finishing procedures.Publishe
mDRONES4rivers-project: UAV-imagery of the project area Nonnenwerth at the Rhine River, Germany
No full textSpatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices.
Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘.
In this dataset, the following UAS data and metadata of the project site ‘Nonnenwerth’ (center coordinates [WGS84]: 50.637541°N, 7.208834°E; area: 8 ha) at the Rhine River in Germany is available for download:
• Multispectral orthophotos (GeoTiff; 6 bands: B, G, R, Red-Edge, NIR, Flag; camera: Micasense; resolution: 25 cm; abbreviation: MS_RAW)
• RGB-orthophotos (GeoTiff; 3 bands: R, G, B; camera: Phantom; resolution: 25 cm; abbreviation: PH_ORTHO)
• Digital Surface Models (GeoTiff; 1 band; camera: Phantom; resolution: ca. 5 cm; abbreviation: PH_DEM)
• associated Technical Reports (PDF; technical metadata concerning data acquisition, and processing using Agisoft Metashape, 1x for multispectral orthophotos, 1x for RGB-orthophotos + digital surface model)
The above-mentioned files are provided for download as dataset stored in one directory per season depending on the date of data acquisition (e.g. mDRONES4rivers_NW_UAV_2019_01_Winter.zip = projectname_projectsite_platform_year_no.season_name.season). To provide an overview of all files and general background information plus data preview the following files are stored in the info.zip folder:
• Overview table and metadata of the above-mentioned data (xlsx)
• Summary (PDF, Detailed description of sensors and data acquisition procedure, 1x for multispectral orthophotos, 1x for RGB-orthophotos + digital surface models)
Note: the data was processed with focus on spectral information and not for geodetic purposes. Georeferencing accuracy has not been checked in detail.This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Gilles Roc
